Model predictive economic/environmental dispatch of power systems with intermittent resources

This paper presents potential benefits of applying model predictive control (MPC) to solving the multi-objective economic/environmental dispatch problem in electric power systems with many intermittent resources. Based on the predictive model of the available output in the next short time period (e.g. 5 minutes) from the intermittent resources, this paper introduces a look-ahead optimal control algorithm for dispatching the available generation resources with the objective of minimizing objective function comprising both generation and environmental costs. This method is compared with (1) the static economic dispatch which treats intermittent resources as uncertain negative loads, and (2) the MPC dispatch with single objective function of minimizing the total generation cost. We show that the proposed MPC approach could lower the generation costs by directly dispatching the generator output from the renewable resources in order to compensate temporal load variations over pre-defined time horizon. Furthermore, the multi-objective economic/environmental cost function provides a formulation to study the tradeoff of efficiency and environmental impact in future energy systems. Simulation is implemented in a 12-bus power system comprising five generators to illustrate potential benefits from this look-ahead dispatch of both intermittent and more conventional power plants. The proposed method is directly applicable to managing power systems with large presence of wind and photovoltaic resources.

[1]  Jan Dimon Bendtsen,et al.  Introducing Model Predictive Control for Improving Power Plant Portfolio Performance , 2008 .

[2]  Bruce H. Krogh,et al.  Distributed model predictive control , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[3]  Sumedha,et al.  SHORT COMMUNICATION , 2007 .

[4]  Gaudenz Alesch Koeppel.  Reliability considerations of future energy systems , 2007 .

[5]  C. Siriopoulos,et al.  Time series forecasting of averaged data with efficient use of information , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[6]  Michael Milligan,et al.  Wind Energy and Power System Operations: A Survey of Current Research and Regulatory Actions , 2002 .

[7]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .

[8]  M.D. Ilic,et al.  Modeling future cyber-physical energy systems , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[9]  Michio Sadatomi,et al.  Prediction of photovoltaic power output considering weather conditions , 2006 .

[10]  W.L. Kling,et al.  Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch , 2007, IEEE Transactions on Energy Conversion.

[11]  Toshihisa Shimizu,et al.  Generation control circuit for photovoltaic modules , 2001 .

[12]  Ferial El-Hawary,et al.  A summary of environmental/economic dispatch algorithms , 1994 .

[13]  Jon Clare,et al.  Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation , 1996 .

[14]  F. Bouffard,et al.  Stochastic security for operations planning with significant wind power generation , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[15]  J.A. Momoh,et al.  Optimal power dispatch of photovoltaic system with random load , 2004, IEEE Power Engineering Society General Meeting, 2004..

[16]  S. Morton,et al.  Model Predictive Control and the Optimization of Power Plant Load while Considering Lifetime Consumption , 2001, IEEE Power Engineering Review.

[17]  G. Andersson,et al.  Towards multi-source multi-product energy systems , 2007 .

[18]  Marko Bacic,et al.  Model predictive control , 2003 .